Discrete cosine transform (DCT) based data compression techniques, e.g. JPEG and MPEG, are widely used in the field of video/image processing. However, annoying effects resulting from ringing, block noise, and other types of noise occurrences are known to appear in compressed images with low bit rates. Post-processing is often applied to the output images to reduce these artifacts to enhance the image quality. Although these post-processing techniques reduce some noise components, they are often overly complex or inadequate to restore high image quality.
FIG. 1 illustrates a JPEG compressed image with low bit rate of 0.678 bits-per-pixel. The image shows large distortions at various regions. Region A at the intersection of bands 101 and 102 shows a wavy noise near a sharp edge. Region B at the intersection of bands 103 and 104 includes prominent noises near a DCT-block boundary. The noise in region A is called ringing noise. The noise in region B is called block noise. These two noise types show following characteristics. Ringing noise is a wavy noise near a sharp edge. Block noise is a large gap along a DCT block boundary but with no wavy texture in the long range.
Conventional linear filters are commonly used to eliminate high frequency components of decoded image. Since most noise sources have strong contributions in the high frequency spectrum, low pass filtering reduces the noise artifacts. However, low pass filtering removes image detail, which also has high frequency spectrum contributions. The result of low pass filtering is sometimes an unduly dull output image.
Wavelet analysis is employed to reduce the artifacts in DCT-based compressed images. This analysis often produces high quality output images but often requires costly complex computational resources.